EBDMSS

2012 ◽  
pp. 265-284
Author(s):  
Fen Wang ◽  
Natalie Lupton ◽  
David Rawlinson ◽  
Xingguo Zhang
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Support System ◽  
Balanced Scorecard ◽  
Business Strategy ◽  
Business Managers ◽  
Web Based ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Wide Range

This paper describes a Web-based intelligent decision making support system (DMSS) to deliver balanced scorecard (BSC) based modelling and analysis in support of strategic E-business management. This framework supports E-business managers during the strategy making process in a comprehensive, integrated, and continuous manner. The paper demonstrates how practitioners can use this system to deliver a wide range of embodied E-business strategy expertise in support of real-time decision making.

EBDMSS

2011 ◽  
pp. 549-568
Author(s):  
Fen Wang ◽  
Natalie Lupton ◽  
David Rawlinson ◽  
Xingguo Zhang
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Support System ◽  
Balanced Scorecard ◽  
Business Strategy ◽  
Business Managers ◽  
Web Based ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Wide Range

This paper describes a Web-based intelligent decision making support system (DMSS) to deliver balanced scorecard (BSC) based modelling and analysis in support of strategic E-business management. This framework supports E-business managers during the strategy making process in a comprehensive, integrated, and continuous manner. The paper demonstrates how practitioners can use this system to deliver a wide range of embodied E-business strategy expertise in support of real-time decision making.

EBDMSS

2010 ◽  
Vol 2 (4) ◽  
pp. 50-68 ◽  
Author(s):  
Fen Wang ◽  
Natalie Lupton ◽  
David Rawlinson ◽  
Xingguo Zhang
Keyword(s):  
Decision Making ◽  
Support System ◽  
Balanced Scorecard ◽  
Business Strategy ◽  
Business Management ◽  
Business Managers ◽  
Web Based ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Wide Range

This paper describes a Web-based intelligent decision making support system (DMSS) to deliver balanced scorecard (BSC) based modelling and analysis in support of strategic E-business management. This framework supports E-business managers during the strategy making process in a comprehensive, integrated, and continuous manner. The paper demonstrates how practitioners can use this system to deliver a wide range of embodied E-business strategy expertise in support of real-time decision making.

scholarly journals IoT in Online Banking

2021 ◽  
Vol 2 (4) ◽  
pp. 219-222
Author(s):  
Malti Bansal ◽  
Naman Oberoi ◽  
Mohd. Sameer
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Market Share ◽  
Online Banking ◽  
Huge Amount ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Banking Institutions ◽  
Physical Objects

As we know, there are so many changes arriving right now ion the banking industries which are really complex industries. Every day, huge amount of data is processed and gathered. With this increase in size, it is becoming more difficult for banking institutions to manage this data and handle other segments of their business. This paper presents the scope of IoT in the banking domain and how various transformations could potentially bring game changing reforms in the traditional methodology. Banking institutions need to integrate IoT in their systems to increase their market share by providing services catered to a clients need based on the data that’s being processed in real time. In future, IoT will be able to create such technologies which will be able to connect physical objects so that objects can do their own intelligent decision making.

scholarly journals Intelligent decision-making support system for manufacturing solution recommendation in a cloud framework

2020 ◽  
Author(s):  
Alessandro Simeone ◽  
Yunfeng Zeng ◽  
Alessandra Caggiano
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Support System ◽  
Cloud Manufacturing ◽  
Cloud Platform ◽  
Intelligent Decision Making ◽  
System A ◽  
Intelligent Decision ◽  
Learning Procedure ◽  
Decision Making Support

AbstractCloud manufacturing represents a valuable tool to enable wide sharing of manufacturing services and solutions by connecting suppliers and customers in large-scale manufacturing networks through a cloud platform. In this context, with increasing manufacturing network size at global scale, the elevated number of manufacturing solutions offered via cloud platform to connected customers can increase the complexity of decision-making, resulting in poor user experience from a customer perspective. To tackle this issue, in this paper, an intelligent decision-making support tool based on a manufacturing service recommendation system (RS) is designed and developed to provide for tailored manufacturing solution recommendation to customers in a cloud manufacturing system. A machine learning procedure based on neural networks for data regression is employed to process historical data on user manufacturing solution preferences and to carry out the automatic extraction of key features from incoming user instances and compatible manufacturing solutions generated by the cloud platform. In this way, the machine learning procedure is able to perform a customer segmentation and build a recommendation list characterized by a ranking of manufacturing solutions which is tailored to the specific customer profile. With the aim to validate the proposed intelligent decision-making support system, a case study is simulated within the framework of a cloud manufacturing platform delivering dynamic sharing of sheet metal cutting manufacturing solutions. The system capability is discussed in terms of machine learning performance as well as industrial applicability and user selection likelihood.

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